Site-specific modeling tools for predicting the impact of corrupting mainbeam targets on STAP

Abstract

This paper provides details about modeling tools being developed under the Defense Advanced Research Projects Agency's (DARPA) knowledge-based sensor signal processing and expert reasoning (KASSPER) program to efficiently predict the performance of GMTS sensors operating in real-world environments. Specifically this paper addresses model to compute losses due to targets corrupting the training data (W.L. Melvin and J.R. Guerci, May 2001) (J.S. Bergin et al., April 2002) for airborne radars that employ space-time adaptive processing (STAP) (J. Ward, December 1994). The modeling tools can be used to predict losses in a computationally efficient manner and therefore allow analysis of GMTI performance for realistic simulation scenarios that span very long time periods.